#include <iostream>
#include <opencv2/core/core.hpp>//opencv核心模块
#include <opencv2/features2d/features2d.hpp>//opencv特征点
#include <opencv2/highgui/highgui.hpp>//opencv gui
#include <opencv2/calib3d/calib3d.hpp>//求解器头文件
#include <Eigen/Core>//eigen核心模块
#include <g2o/core/base_vertex.h>//g2o顶点（Vertex）头文件 视觉slam十四讲p141用顶点表示优化变量，用边表示误差项
#include <g2o/core/base_unary_edge.h>//g2o边（edge）头文件
#include <g2o/core/sparse_optimizer.h>//稠密矩阵求解
#include <g2o/core/block_solver.h>//求解器头文件
#include <g2o/core/solver.h>
#include <g2o/core/optimization_algorithm_gauss_newton.h>//高斯牛顿算法头文件
#include <g2o/solvers/dense/linear_solver_dense.h>//线性求解
#include <sophus/se3.hpp>//李群李代数se3
#include <chrono>

using namespace std;
using namespace cv;

void find_feature_matches(
        const Mat &img_1, const Mat &img_2,
        std::vector<KeyPoint> &keypoints_1,
        std::vector<KeyPoint> &keypoints_2,
        std::vector<DMatch> &matches);//定义find_feature_matches函数 输入图像1和图像2，输出特征点集合1、特征点集合2和匹配点对

// 像素坐标转相机归一化坐标
Point2d pixel2cam(const Point2d &p, const Mat &K);

// BA by g2o
typedef vector<Eigen::Vector2d, Eigen::aligned_allocator<Eigen::Vector2d>> VecVector2d;//2d点 见视觉slam十四讲 p180-p187 需要输入2d点
typedef vector<Eigen::Vector3d, Eigen::aligned_allocator<Eigen::Vector3d>> VecVector3d;//3d点 见视觉slam十四讲 p180-p187 需要输入3d点

void bundleAdjustmentG2O(
        const VecVector3d &points_3d,
        const VecVector2d &points_2d,
        const Mat &K,
        Sophus::SE3d &pose
); //定义bundleAdjustmentG2O函数 输入3d点和2d点和相机内参矩阵，输出pose为优化变量 使用BA方法

// BA by gauss-newton
void bundleAdjustmentGaussNewton(
        const VecVector3d &points_3d,
        const VecVector2d &points_2d,
        const Mat &K,
        Sophus::SE3d &pose
);//定义bundleAdjustmentGaussNewton函数 输入3d点和2d点和相机内参矩阵，输出pose为优化变量 使用高斯牛顿法

int main(int argc, char **argv) {
    if (argc != 5) {
        cout << "usage: pose_estimation_3d2d img1 img2 depth1 depth2" << endl;//输出命令行用法
        return 1;
    }
    //-- 读取图像
    Mat img_1 = imread(argv[1], CV_LOAD_IMAGE_COLOR);//读取图像1  CV_LOAD_IMAGE_COLOR表示返回一张彩色图像
    Mat img_2 = imread(argv[2], CV_LOAD_IMAGE_COLOR);//读取图像2  CV_LOAD_IMAGE_COLOR表示返回一张彩色图像
    assert(img_1.data && img_2.data && "Can not load images!");//assert()为断言函数，条件为假则停止执行

    vector<KeyPoint> keypoints_1, keypoints_2;//特征点1 -> 图像1 特征点2 -> 图像2
    vector<DMatch> matches;//匹配 matches
    find_feature_matches(img_1, img_2, keypoints_1, keypoints_2, matches);//调用find_feature_matches函数
    cout << "一共找到了" << matches.size() << "组匹配点" << endl;//输出匹配点数

//************************调用opencv函数求解pnp******************************
    // 建立3D点
    Mat d1 = imread(argv[3], CV_LOAD_IMAGE_UNCHANGED);       // 深度图为16位无符号数，单通道图像
    Mat K = (Mat_<double>(3, 3) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1);//初始化相机内参矩阵
    vector<Point3f> pts_3d;//3d路标点
    vector<Point2f> pts_2d;//2d像素点
    for (DMatch m:matches) {
        ushort d = d1.ptr<unsigned short>(int(keypoints_1[m.queryIdx].pt.y))[int(keypoints_1[m.queryIdx].pt.x)];
        if (d == 0)   // bad depth
            continue;
        float dd = d / 5000.0;
        Point2d p1 = pixel2cam(keypoints_1[m.queryIdx].pt, K);//像素坐标转相机归一化坐标
        pts_3d.push_back(Point3f(p1.x * dd, p1.y * dd, dd));
        pts_2d.push_back(keypoints_2[m.trainIdx].pt);
    }

    cout << "3d-2d pairs: " << pts_3d.size() << endl;//输出3d-2d pairs

    chrono::steady_clock::time_point t1 = chrono::steady_clock::now();//开始计时
    Mat r, t;//r表示旋转向量 t表示平移向量
    solvePnP(pts_3d, pts_2d, K, Mat(), r, t, false); // 调用OpenCV 的 PnP 求解，可选择EPNP，DLS等方法
    Mat R;//旋转矩阵R
    cv::Rodrigues(r, R); // r为旋转向量形式，用Rodrigues公式转换为矩阵 视觉slam十四讲p53式3.15
    chrono::steady_clock::time_point t2 = chrono::steady_clock::now();//计时结束
    chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);//计算耗时
    cout << "solve pnp in opencv cost time: " << time_used.count() << " seconds." << endl;//输出使用opencv中使用pnp所花费的时间

    cout << "R=" << endl << R << endl;//输出旋转矩阵
    cout << "t=" << endl << t << endl;//输出平移向量

//*************************使用BA方法*****************************************
    VecVector3d pts_3d_eigen;
    VecVector2d pts_2d_eigen;
    for (size_t i = 0; i < pts_3d.size(); ++i) //遍历3d点
    {
        pts_3d_eigen.push_back(Eigen::Vector3d(pts_3d[i].x, pts_3d[i].y, pts_3d[i].z));
        pts_2d_eigen.push_back(Eigen::Vector2d(pts_2d[i].x, pts_2d[i].y));
    }

    cout << "calling bundle adjustment by gauss newton" << endl;//输出calling bundle adjustment by gauss newton
    Sophus::SE3d pose_gn;
    t1 = chrono::steady_clock::now();//计时开始
    bundleAdjustmentGaussNewton(pts_3d_eigen, pts_2d_eigen, K, pose_gn);//调用bundleAdjustmentGaussNewton函数
    t2 = chrono::steady_clock::now();//计时结束
    time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);//计时结束
    cout << "solve pnp by gauss newton cost time: " << time_used.count() << " seconds." << endl;//输出

    cout << "calling bundle adjustment by g2o" << endl;
    Sophus::SE3d pose_g2o;
    t1 = chrono::steady_clock::now();
    bundleAdjustmentG2O(pts_3d_eigen, pts_2d_eigen, K, pose_g2o);
    t2 = chrono::steady_clock::now();
    time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
    cout << "solve pnp by g2o cost time: " << time_used.count() << " seconds." << endl;//输出在G2O中求解pnp耗时
    return 0;
}

void find_feature_matches(const Mat &img_1, const Mat &img_2,
                          std::vector<KeyPoint> &keypoints_1,
                          std::vector<KeyPoint> &keypoints_2,
                          std::vector<DMatch> &matches) {
    //-- 初始化
    Mat descriptors_1, descriptors_2;//描述子和描述子2
    // used in OpenCV3
    Ptr<FeatureDetector> detector = ORB::create();
    Ptr<DescriptorExtractor> descriptor = ORB::create();
    // use this if you are in OpenCV2
    // Ptr<FeatureDetector> detector = FeatureDetector::create ( "ORB" );
    // Ptr<DescriptorExtractor> descriptor = DescriptorExtractor::create ( "ORB" );
    Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");//使用汉明距离进行特征匹配
    //-- 第一步:检测 Oriented FAST 角点位置
    detector->detect(img_1, keypoints_1);
    detector->detect(img_2, keypoints_2);

    //-- 第二步:根据角点位置计算 BRIEF 描述子
    descriptor->compute(img_1, keypoints_1, descriptors_1);//检测图像1的 Oriented FAST 角点
    descriptor->compute(img_2, keypoints_2, descriptors_2);//检测图像2的 Oriented FAST 角点

    //-- 第三步:对两幅图像中的BRIEF描述子进行匹配，使用 Hamming 距离
    vector<DMatch> match;
    // BFMatcher matcher ( NORM_HAMMING );
    matcher->match(descriptors_1, descriptors_2, match);

    //-- 第四步:匹配点对筛选
    double min_dist = 10000, max_dist = 0;//求最小距离和最大距离

    //找出所有匹配之间的最小距离和最大距离, 即是最相似的和最不相似的两组点之间的距离
    for (int i = 0; i < descriptors_1.rows; i++) {
        double dist = match[i].distance;
        if (dist < min_dist) min_dist = dist;
        if (dist > max_dist) max_dist = dist;
    }

    printf("-- Max dist : %f \n", max_dist);
    printf("-- Min dist : %f \n", min_dist);

    //当描述子之间的距离大于两倍的最小距离时,即认为匹配有误.但有时候最小距离会非常小,设置一个经验值30作为下限.
    //遍历所有匹配，排除误匹配
    for (int i = 0; i < descriptors_1.rows; i++) {
        if (match[i].distance <= max(2 * min_dist, 30.0))//不同的结果可以在这里设置
            //当描述子之间的距离大于两倍的最小距离时,即认为匹配有误 30.0为经验值
        {
            matches.push_back(match[i]);
        }
    }
}

Point2d pixel2cam(const Point2d &p, const Mat &K)
{
    return Point2d
            (
                    (p.x - K.at<double>(0, 2)) / K.at<double>(0, 0),
                    (p.y - K.at<double>(1, 2)) / K.at<double>(1, 1)
            );
}

void bundleAdjustmentGaussNewton(
        const VecVector3d &points_3d,
        const VecVector2d &points_2d,
        const Mat &K,
        Sophus::SE3d &pose) {
    typedef Eigen::Matrix<double, 6, 1> Vector6d;//6*1矩阵
    const int iterations = 10;//迭代次数设为10
    double cost = 0, lastCost = 0;
    double fx = K.at<double>(0, 0);//相机内参 具体含义见视觉slam十四讲p99
    double fy = K.at<double>(1, 1);//相机内参 具体含义见视觉slam十四讲p99
    double cx = K.at<double>(0, 2);//相机内参 具体含义见视觉slam十四讲p99
    double cy = K.at<double>(1, 2);//相机内参 具体含义见视觉slam十四讲p99

    for (int iter = 0; iter < iterations; iter++)//迭代
    {
        Eigen::Matrix<double, 6, 6> H = Eigen::Matrix<double, 6, 6>::Zero();//将6*6矩阵初始化为0
        Vector6d b = Vector6d::Zero();

        cost = 0;//代价函数值置为0
        // compute cost
        for (int i = 0; i < points_3d.size(); i++)//遍历3d点
            //计算增量方程中的H和b以及代价函数值
        {
            Eigen::Vector3d pc = pose * points_3d[i];//P' = TP =[X',Y',Z'] 视觉slam十四讲p186式7.38
            double inv_z = 1.0 / pc[2];//相当于1 / Z'
            double inv_z2 = inv_z * inv_z;//相当于( 1 / Z' ) * ( 1 / Z' )
            Eigen::Vector2d proj(fx * pc[0] / pc[2] + cx, fy * pc[1] / pc[2] + cy);//u = fx(X' / z') +cx,v = fy(Y' / z') +cy 视觉slam十四讲p186式7.41


            Eigen::Vector2d e = points_2d[i] - proj;

            cost += e.squaredNorm();
            Eigen::Matrix<double, 2, 6> J;//2*6雅克比矩阵 视觉slam十四讲p186式7.46
            //雅克比矩阵表达式见 视觉slam十四讲p186式7.46
            J << -fx * inv_z,
                    0,
                    fx * pc[0] * inv_z2,
                    fx * pc[0] * pc[1] * inv_z2,
                    -fx - fx * pc[0] * pc[0] * inv_z2,
                    fx * pc[1] * inv_z,
                    0,
                    -fy * inv_z,
                    fy * pc[1] * inv_z2,
                    fy + fy * pc[1] * pc[1] * inv_z2,
                    -fy * pc[0] * pc[1] * inv_z2,
                    -fy * pc[0] * inv_z;

            H += J.transpose() * J;
            b += -J.transpose() * e;
        }
        // -fx * inv_z 相当于-fx / Z'
        //0
        //fx * pc[0] * inv_z2相当于fx * X' / ( Z' * Z' )
        //-fx - fx * pc[0] * pc[0] * inv_z2相当于fx * X' * Y' / ( Z' * Z')
        //fx * pc[1] * inv_z相当于fx * Y' / Z'
        //0
        //-fy * inv_z相当于-fy / Z'
        //fy * pc[1] * inv_z2相当于fy * Y' / (Z' * Z')
        //fy + fy * pc[1] * pc[1] * inv_z2相当于fy + fy * Y'*Y' / (Z' * Z')
        //-fy * pc[0] * pc[1] * inv_z2相当于fy * X' * Y' / ( Z' * Z')
        //-fy * pc[0] * inv_z相当于-fy * X' / Z'

        Vector6d dx;
        dx = H.ldlt().solve(b);//LDT求解

        if (isnan(dx[0])) {
            cout << "result is nan!" << endl;
            break;
        }

        if (iter > 0 && cost >= lastCost) {
            // cost increase, update is not good
            cout << "cost: " << cost << ", last cost: " << lastCost << endl;//输出代价
            break;
        }

        // update your estimation
        pose = Sophus::SE3d::exp(dx) * pose;
        lastCost = cost;

        cout << "iteration " << iter << " cost=" << std::setprecision(12) << cost << endl;//输出迭代次数和代价
        if (dx.norm() < 1e-6) {
            // converge
            break;
        }
    }

    cout << "pose by g-n: \n" << pose.matrix() << endl;//输出pose by g-n
}

/// vertex and edges used in g2o ba
// 曲线模型的顶点，模板参数：优化变量维度和数据类型
class VertexPose : public g2o::BaseVertex<6, Sophus::SE3d>//:表示继承，public表示公有继承；CurveFittingVertex是派生类，:BaseVertex<6, Sophus::SE3d>是基类
{
public://以下定义的成员变量和成员函数都是公有的
    EIGEN_MAKE_ALIGNED_OPERATOR_NEW;//解决Eigen库数据结构内存对齐问题
    // 重置
    virtual void setToOriginImpl() override //virtual表示该函数为虚函数，override保留字表示当前函数重写了基类的虚函数
    {
        _estimate = Sophus::SE3d();
    }

    // left multiplication on SE3
    // 更新
    virtual void oplusImpl(const double *update) override {
        Eigen::Matrix<double, 6, 1> update_eigen;
        update_eigen << update[0], update[1], update[2], update[3], update[4], update[5];
        _estimate = Sophus::SE3d::exp(update_eigen) * _estimate;
    }
    // 存盘和读盘：留空
    virtual bool read(istream &in) override {}//istream类是c++标准输入流的一个基类
    //可参照C++ Primer Plus第六版的6.8节

    virtual bool write(ostream &out) const override {}//ostream类是c++标准输出流的一个基类
    //可参照C++ Primer Plus第六版的6.8节
};
// 误差模型 模板参数：观测值维度，类型，连接顶点类型
class EdgeProjection : public g2o::BaseUnaryEdge<2, Eigen::Vector2d, VertexPose> {
public:
    EIGEN_MAKE_ALIGNED_OPERATOR_NEW;//解决Eigen库数据结构内存对齐问题

    EdgeProjection(const Eigen::Vector3d &pos, const Eigen::Matrix3d &K) : _pos3d(pos), _K(K) {}//使用列表赋初值

    virtual void computeError() override//virtual表示虚函数，保留字override表示当前函数重写了基类的虚函数
    {
        const VertexPose *v = static_cast<VertexPose *> (_vertices[0]);//创建指针v
        Sophus::SE3d T = v->estimate();//将estimate()值赋给V
        Eigen::Vector3d pos_pixel = _K * (T * _pos3d);
        pos_pixel /= pos_pixel[2];
        _error = _measurement - pos_pixel.head<2>();
    }

    virtual void linearizeOplus() override {
        const VertexPose *v = static_cast<VertexPose *> (_vertices[0]);
        Sophus::SE3d T = v->estimate();
        Eigen::Vector3d pos_cam = T * _pos3d;
        double fx = _K(0, 0);
        double fy = _K(1, 1);
        double cx = _K(0, 2);
        double cy = _K(1, 2);
        double X = pos_cam[0];
        double Y = pos_cam[1];
        double Z = pos_cam[2];
        double Z2 = Z * Z;
        _jacobianOplusXi
                << -fx / Z, 0, fx * X / Z2, fx * X * Y / Z2, -fx - fx * X * X / Z2, fx * Y / Z,
                0, -fy / Z, fy * Y / (Z * Z), fy + fy * Y * Y / Z2, -fy * X * Y / Z2, -fy * X / Z;
    } //雅克比矩阵表达式见 视觉slam十四讲p186式7.46

    virtual bool read(istream &in) override {}

    virtual bool write(ostream &out) const override {}

private:
    Eigen::Vector3d _pos3d;
    Eigen::Matrix3d _K;
};

void bundleAdjustmentG2O(
        const VecVector3d &points_3d,
        const VecVector2d &points_2d,
        const Mat &K,
        Sophus::SE3d &pose) {

    // 构建图优化，先设定g2o
    typedef g2o::BlockSolver<g2o::BlockSolverTraits<6, 3>> BlockSolverType;  // pose is 6, landmark is 3
    typedef g2o::LinearSolverDense<BlockSolverType::PoseMatrixType> LinearSolverType; // 线性求解器类型
    // 梯度下降方法，可以从GN, LM, DogLeg 中选
    auto solver = new g2o::OptimizationAlgorithmGaussNewton(
            g2o::make_unique<BlockSolverType>(g2o::make_unique<LinearSolverType>()));//c++中的make_unique表示智能指针类型
    g2o::SparseOptimizer optimizer;     // 图模型
    optimizer.setAlgorithm(solver);   // 设置求解器
    optimizer.setVerbose(true);       // 打开调试输出

    // vertex
    // 往图中增加顶点
    VertexPose *vertex_pose = new VertexPose(); // camera vertex_pose
    vertex_pose->setId(0);//对顶点进行编号，里面的0你可以写成任意的正整数，但是后面设置edge连接顶点时，必须要和这个一致
    vertex_pose->setEstimate(Sophus::SE3d());
    optimizer.addVertex(vertex_pose);//添加顶点

    // K 相机内参矩阵
    Eigen::Matrix3d K_eigen;
    K_eigen <<
            K.at<double>(0, 0), K.at<double>(0, 1), K.at<double>(0, 2),
            K.at<double>(1, 0), K.at<double>(1, 1), K.at<double>(1, 2),
            K.at<double>(2, 0), K.at<double>(2, 1), K.at<double>(2, 2);

    // edges
    // 往图中增加边
    int index = 1;
    for (size_t i = 0; i < points_2d.size(); ++i)//遍历2d点
    {
        auto p2d = points_2d[i];
        auto p3d = points_3d[i];
        EdgeProjection *edge = new EdgeProjection(p3d, K_eigen);
        edge->setId(index);//对顶点进行编号，里面的0你可以写成任意的正整数，但是后面设置edge连接顶点时，必须要和这个一致
        edge->setVertex(0, vertex_pose);  // 设置连接的顶点
        edge->setMeasurement(p2d);// 观测数值
        edge->setInformation(Eigen::Matrix2d::Identity());// 信息矩阵：协方差矩阵之逆
        optimizer.addEdge(edge);//添加边
        index++;
    }

    chrono::steady_clock::time_point t1 = chrono::steady_clock::now();//开始计时
    optimizer.setVerbose(true);
    optimizer.initializeOptimization();
    optimizer.optimize(10);//迭代次数10
    chrono::steady_clock::time_point t2 = chrono::steady_clock::now();//计时结束
    chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);//计算耗时
    cout << "optimization costs time: " << time_used.count() << " seconds." << endl;
    cout << "pose estimated by g2o =\n" << vertex_pose->estimate().matrix() << endl;
    pose = vertex_pose->estimate();
}